import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from proto_mdd.utils import euclidean_metric

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)
        self.softmax = nn.Softmax(dim=2)

    def forward(self, q, k, v):

        attn = torch.bmm(q, k.transpose(1, 2))
        attn = attn / self.temperature
        log_attn = F.log_softmax(attn, 2)
        attn = self.softmax(attn)
        attn = self.dropout(attn)
        output = torch.bmm(attn, v)
        return output, attn, log_attn

class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''

    def __init__(self, args, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()
        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v

        self.w_qs = nn.Linear(d_model, n_head * d_k)
        self.w_ks = nn.Linear(d_model, n_head * d_k)
        self.w_vs = nn.Linear(d_model, n_head * d_v)
        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))

        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
        self.layer_norm = nn.LayerNorm(d_model)

        self.fc = nn.Linear(n_head * d_v, d_model)
        nn.init.xavier_normal_(self.fc.weight)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, q, k, v):
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, _ = q.size()
        sz_b, len_k, _ = k.size()
        sz_b, len_v, _ = v.size()

        residual = q
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
        
        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv

        output, attn, log_attn = self.attention(q, k, v)

        output = output.view(n_head, sz_b, len_q, d_v)
        output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv)

        output = self.dropout(self.fc(output))
        output = self.layer_norm(output + residual)

        return output


class proto_attention_net(nn.Module):

    def __init__(self, args, dropout=0.2):
        super().__init__()
        if args.model_type == 'ConvNet':
            from proto_mdd.networks.convnet import ConvNet
            self.encoder = ConvNet()
            z_dim = 64
        elif args.model_type == 'ResNet':
            from proto_mdd.networks.resnet import ResNet
            self.encoder = ResNet()
            z_dim = 640
        elif args.model_type == 'ResNet18':
            hdim = 512
            z_dim = 512
            from proto_mdd.networks.resnet_pytorch import resnet18
            self.encoder = resnet18(pretrained = False)
        else:
            raise ValueError('')

        self.slf_attn = MultiHeadAttention(args, args.head, z_dim, z_dim, z_dim, dropout=dropout)    
        self.z_dim = z_dim
        self.args = args

    def forward(self, support, query, input_type = "data"):

        if input_type == "data":
            # feature extraction
            support = self.encoder(support)
            query = self.encoder(query) 
            proto = support.reshape(self.args.shot, -1, support.shape[-1]).mean(dim=0) # N x d                            
            
            # refine by attention machenism
            proto = proto.unsqueeze(0)  # 1 x N x d                    
            proto = self.slf_attn(proto, proto, proto)
            proto = proto.squeeze(0)
            
            # compute distance for all batches
            logitis = euclidean_metric(query, proto) / self.args.temperature
                                  
            return support, query, logitis

        elif input_type == "feature":                
            # get mean of the support
            proto = support.reshape(self.args.shot, -1, support.shape[-1]).mean(dim=0) # N x d                       
            # refine by attention machenism
            proto = proto.unsqueeze(0)  # 1 x N x d                    
            proto = self.slf_attn(proto, proto, proto)
            proto = proto.squeeze(0)
            
            # compute distance for all batches
            logitis = euclidean_metric(query, proto) / self.args.temperature

            return logitis

